An advantage of digital photography over film is the ability to capture a multitude of images with little or no added expense. As a result, it is common for photographers to accumulate large digital image collections that can be difficult to maintain and difficult to browse serially. Unlike conventional film-based photographs, digital photographs can be loaded onto a computer system. Photographs can then be accessed, organized and manipulated using photograph management software. In managing a collection of digital photographs, it is quite useful to assign labels, or tags, to the photographs, to facilitate subsequent operations involving the photographs. For example, photographs can be labeled with the names of the people that appear in the photographs to facilitate subsequent retrieval of photographs containing a specific person.
It can be very time-consuming to label all of the images in a large collection of digital images, especially when a user must manually label each image. As a semi-automatic process for labeling faces in images, conventional face labeling systems use traditional face recognition techniques to detect and match faces in a set of images. Based on the face recognition results, a conventional face labeling system provides suggested labels for specific faces to a user. For example, the conventional face labeling system displays a number of faces that are similar to a labeled face and instructs the user to confirm whether the label applies to all of the displayed faces.
Such conventional methods do not utilize the full knowledge of the face recognition engine, as this method of suggesting specific labels requires a strict threshold to determine whether unlabeled faces are presented to a user with a suggested label. For example, the conventional face labeling system may be somewhat confident in a face match, but not confident enough to display the faces as a suggested match. The conventional face labeling system must adhere to the strict threshold, and, thus, has no means to indicate partial confidence in a face match. Accordingly, a conventional face labeling system is typically either too conservative or too liberal in providing face label suggestions. With a conservative setting, a conventional face labeling system will only suggest labels and faces which are highly likely to be a match. This approach results in a lot of work for the user, as the system will not display many suggested faces and the user will need to manually label many faces. With a liberal setting, a conventional face labeling system will display labels and faces that have a low likelihood of being a match. This approach will result in frustration for the user, as the user will be required to correct many mistakes made by the conventional face labeling system. Furthermore, conventional systems require context-switching, as multiple different user interface windows are required as users provide label inputs for different groups of suggested faces.